A Next‐Generation Snow Albedo Parameterization for Climate Modeling Using Constrained Machine Learning

Abstract

We demonstrate a data‐driven parameterization framework for snow albedo evolution using a constrained auto‐regressive neural differential equation that directly predicts snow albedo change from standard meteorological inputs. After training with multi‐year in situ and satellite observations from a wide variety of locations, the scheme effectively reproduces daily albedo evolution across diverse climate zones, with median error under 7.5% (RMSE ≈ 0.05), a 10%–30% improvement over established models. Furthermore, the model generalizes to sites not seen during training and scales from coarser grids to point locations. The scheme can easily incorporate new features as observational networks expand, offering an adaptive and computationally lightweight framework for next‐generation land and climate models.